Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Wendy D. W. T. Bayu"'
Autor:
Ari Wibisono, Petrus Mursanto, Jihan Adibah, Wendy D. W. T. Bayu, May Iffah Rizki, Lintang Matahari Hasani, Valian Fil Ahli
Publikováno v:
Journal of Big Data, Vol 7, Iss 1, Pp 1-13 (2020)
Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental
Externí odkaz:
https://doaj.org/article/b10356c01ec14fea9a714ac62335aec6
Autor:
Lintang Matahari Hasani, May Iffah Rizki, Jihan Adibah, Petrus Mursanto, Ari Wibisono, Wendy D. W. T. Bayu, Valian Fil Ahli
Publikováno v:
Journal of Big Data, Vol 7, Iss 1, Pp 1-13 (2020)
Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a8ac95588bd63dcbfeeb6b64346aeb02
http://link.springer.com/article/10.1186/s40537-020-00359-w
http://link.springer.com/article/10.1186/s40537-020-00359-w
Autor:
May Iffah Rizki, Wendy D. W. T. Bayu, Petrus Mursanto, Jihan Adibah, Ari Wibisono, Valian Fil Ahli, Lintang Matahari Hasani
Publikováno v:
IWBIS
Big data processing is a challenging job. Extensive time-series data need a method of preparation, management, and feature calculation for each data arrival. FIMT-DD is an algorithm for processing predictive regression for big data. The splitting cri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::88f40de1f75f66868158f3acd4922e5d
https://doi.org/10.1109/iwbis50925.2020.9255607
https://doi.org/10.1109/iwbis50925.2020.9255607
Autor:
Wendy D. W. T. Bayu, Petrus Mursanto, May Iffah Rizki, Valian Fil Ahli, Ari Wibisono, Lintang Matahari Hasani
Publikováno v:
IJCNN
There are some challenges in processing big data, namely multilabel enormous size that the affect may the time and the computing nature of the multilabel which data further complicate the process. In a quest of exploring the may approaches to resolve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9e4b3e03cc43b42df2e03bb63789b659
https://doi.org/10.1109/ijcnn.2019.8852342
https://doi.org/10.1109/ijcnn.2019.8852342